Algorithms and data structures for automatic precision estimation of neural networks
- URL: http://arxiv.org/abs/2509.24607v1
- Date: Mon, 29 Sep 2025 11:13:29 GMT
- Title: Algorithms and data structures for automatic precision estimation of neural networks
- Authors: Igor V. Netay,
- Abstract summary: We extend a neural network library with automatic precision estimation for floating point computations.<n>We discuss conditions to make estimations exact and preserve high computation performance of neural networks training and inference.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We describe algorithms and data structures to extend a neural network library with automatic precision estimation for floating point computations. We also discuss conditions to make estimations exact and preserve high computation performance of neural networks training and inference. Numerical experiments show the consequences of significant precision loss for particular values such as inference, gradients and deviations from mathematically predicted behavior. It turns out that almost any neural network accumulates computational inaccuracies. As a result, its behavior does not coincide with predicted by the mathematical model of neural network. This shows that tracking of computational inaccuracies is important for reliability of inference, training and interpretability of results.
Related papers
- SGD method for entropy error function with smoothing l0 regularization for neural networks [3.108634881604788]
entropy error function has been widely used in neural networks.
We propose a novel entropy function with smoothing l0 regularization for feed-forward neural networks.
Our work is novel as it enables neural networks to learn effectively, producing more accurate predictions.
arXiv Detail & Related papers (2024-05-28T19:54:26Z) - Prediction-Powered Inference [68.97619568620709]
Prediction-powered inference is a framework for performing valid statistical inference when an experimental dataset is supplemented with predictions from a machine-learning system.
The framework yields simple algorithms for computing provably valid confidence intervals for quantities such as means, quantiles, and linear and logistic regression coefficients.
Prediction-powered inference could enable researchers to draw valid and more data-efficient conclusions using machine learning.
arXiv Detail & Related papers (2023-01-23T18:59:28Z) - Refining neural network predictions using background knowledge [68.35246878394702]
We show we can use logical background knowledge in learning system to compensate for a lack of labeled training data.
We introduce differentiable refinement functions that find a corrected prediction close to the original prediction.
This algorithm finds optimal refinements on complex SAT formulas in significantly fewer iterations and frequently finds solutions where gradient descent can not.
arXiv Detail & Related papers (2022-06-10T10:17:59Z) - Cardinality-Minimal Explanations for Monotonic Neural Networks [25.212444848632515]
In this paper, we investigate whether tractability can be regained by focusing on neural models implementing a monotonic function.
Although the relevant decision problems remain intractable, we can show that they become solvable in favourable time.
arXiv Detail & Related papers (2022-05-19T23:47:25Z) - Scalable computation of prediction intervals for neural networks via
matrix sketching [79.44177623781043]
Existing algorithms for uncertainty estimation require modifying the model architecture and training procedure.
This work proposes a new algorithm that can be applied to a given trained neural network and produces approximate prediction intervals.
arXiv Detail & Related papers (2022-05-06T13:18:31Z) - Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs.
By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
arXiv Detail & Related papers (2022-02-01T17:11:13Z) - Multigoal-oriented dual-weighted-residual error estimation using deep
neural networks [0.0]
Deep learning is considered as a powerful tool with high flexibility to approximate functions.
Our approach is based on a posteriori error estimation in which the adjoint problem is solved for the error localization.
An efficient and easy to implement algorithm is developed to obtain a posteriori error estimate for multiple goal functionals.
arXiv Detail & Related papers (2021-12-21T16:59:44Z) - Convolutional generative adversarial imputation networks for
spatio-temporal missing data in storm surge simulations [86.5302150777089]
Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods.
We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
arXiv Detail & Related papers (2021-11-03T03:50:48Z) - Accuracy of neural networks for the simulation of chaotic dynamics:
precision of training data vs precision of the algorithm [0.0]
We simulate the Lorenz system with different precisions using three different neural network techniques adapted to time series.
Our results show that the ESN network is better at predicting accurately the dynamics of the system.
arXiv Detail & Related papers (2020-07-08T17:25:37Z) - Understanding the Effects of Data Parallelism and Sparsity on Neural
Network Training [126.49572353148262]
We study two factors in neural network training: data parallelism and sparsity.
Despite their promising benefits, understanding of their effects on neural network training remains elusive.
arXiv Detail & Related papers (2020-03-25T10:49:22Z) - Understanding and mitigating gradient pathologies in physics-informed
neural networks [2.1485350418225244]
This work focuses on the effectiveness of physics-informed neural networks in predicting outcomes of physical systems and discovering hidden physics from noisy data.
We present a learning rate annealing algorithm that utilizes gradient statistics during model training to balance the interplay between different terms in composite loss functions.
We also propose a novel neural network architecture that is more resilient to such gradient pathologies.
arXiv Detail & Related papers (2020-01-13T21:23:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.